Investors spill what they aren't looking for anymore in AI SaaS companies | TechCrunch

"Popular SaaS categories for investors now include startups building AI-native infrastructure, vertical SaaS with proprietary data, systems of action (those helping users complete tasks), and platforms deeply embedded in mission-critical workflows, according to Aaron Holiday, a managing partner at 645 Ventures."

"If your differentiation lives mostly in UI [user interface] and automation, that's no longer enough. The barrier to entry has dropped, which makes building a real moat much harder, according to Igor Ryabenky, a founder and managing partner at AltaIR Capital."

"New companies entering the market now need to build around real workflow ownership and a clear understanding of the problem from day one. Massive codebases are no longer an advantage. What matters more is speed, focus, and the ability to adapt quickly."

Investor interest in AI startups has become increasingly selective despite massive capital flowing into the sector. Venture capitalists now prioritize AI-native infrastructure, vertical SaaS with proprietary data advantages, systems enabling task completion, and platforms embedded in critical workflows. Conversely, startups building thin workflow layers, generic horizontal tools, basic product management, and surface-level analytics face investor skepticism. Generic vertical software lacking proprietary data moats and products relying primarily on UI and automation differentiation are considered uncompetitive. Successful new entrants must demonstrate real workflow ownership, deep product understanding, rapid adaptation capability, and flexible pricing models rather than relying on large codebases or rigid per-seat pricing structures.

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